Two Models of Change-- A Case Study of Red Stone National Park, China

摘要：ABSTRACT: Using slides of landscapes from the Red Stone National Park, China, two different visual perceptual and landscape change models were found by interviewing twenty-eight groups of Chinese subjects composed of both rural and urban subjects and one group of Western designers: a rural model and an urban model. The former represents the visual perceptual process of local people having a lower educational level, and the latter represents the visual perceptual processes of the urban dwellers and landscape architects having a higher education level . The rural model is responsible for the current construction of tourist services in this national park, including luxurious hotels and exotic resorts, which are not appreciated by urban groups. The model representing the urban dwellers and landscape architects may also be unacceptable to the local people as this model favors traditional villages and farm gardening, considered backward by the local people who seek modernization.

关键词：Tourism impact, visual perception, tourist development, cultural variation in landscape preference, landscape change

(Reprinted from: Proceedings for the 32nd IFLA World Congress, Bankok, Tailand. 273-76,1995)

INTRODUCTION
The dramatic development of national and international tourism in China beginning in the early 1980s has had strong impacts on the landscapes as well as on the profession of landscape architecture. "Destructive construction" (Zhu 1982; Chen 1988; Sun 1985; Gan 1988) has become a serious problem for national scenic and historical landscapes. Many factors may contribute to this "destructive construction" in the process of tourist development. It is hypothesized that some of the "destructive constructions" is caused by cognitive problems, namely the constructors may be unaware of the destructive nature of their work, or even look upon these constructions as improvements in the landscape. They might believe that what they perceive as the most attractive constructions must be equally attractive to the tourists who travel a long distance to see these parks.
In most cases national parks are located in remote areas, and tourist development in these areas is usually carried out by local people who directly manage the landscape. It is thus reasonable to hypothesize that the local people who develop and make changes in the landscapes might have different preference and change models than the tourists who actually use the landscapes. Testing this hypothesis is the main objective of this research.
PROCEDURES
Preference Rating
In total 50 different scenes (color slides) were selected for this study, randomly coded from 1 to 50. Scenes are from the Red Stone National Park in South China's Guangdong Province. The slides were expected to be most representative of the diverse landscape features and spatial dimensions seen in this park.
Twenty nine groups, in total 573 subjects, were invited to participate in the experiment (Yu 1995); one of the groups is composed of graduate students at Harvard Graduate School of Design (GSD, n=24), all other twenty eight groups were composed of Chinese subjects. These participants have very different cultural backgrounds in terms of macro-cultural influence (Western vs. Chinese), landscape expertise (experts vs. general public), living environment (rural vs. urban), educational level (high vs. low) and gender (male vs. female)
Using the SBE procedure (Daniel and Boster. 1976), members from each of twenty-nine groups were asked to rate the 50 scenes (slides) on 1 to 10 point scale. Each slide was shown for 10 seconds. The SBE procedure has been widely recognized as an effective and reliable procedure (Hull, Buhyoff et al. 1984; Schroeder 1984; Hull 1986).. Experiments on the Chinese groups were carried out in Beijing, China; the experiment on the GSD(24) was carried out in Cambridge, MA, USA. Correlation and factor analyses were carried out to investigate the cultural and sub-cultural variations in landscape preference (Yu 1995).
Landscape Categorization Based on Preference Ratings
In my case, an empirical approach to landscape categorization was used. This approach uses observers' preference ratings as indicators to categorize landscapes. This approach has become more and more recognized and proven to be meaningful and practically more relevant than others (Kaplan 1985; Amedeo, Pitt et al. 1989) Underlying this approach is the presumption that some physical features of landscapes and their combinations significantly influence the scenic value (preference level) discrimination; furthermore, observers have internally consistent responses to certain landscape features and their combinations.
The commonly used method in this approach to landscape categorization is factor and cluster analysis. Using the preference ratings for 50 individual slides by twenty-nine subject groups,. a principle component factor analysis model using a varimax rotation method was used to interpret and categorize the landscapes.

Preference Models: Regression with Dumming Variables
On the basis of landscape categorization using factor analysis and clustering, landscape compositions of individual scenes are analyzed by using the principle factors as binary (dummy) variables (Jobson 1992; Hardy 1993). Regressional models were built for landscape preference prediction based on these dumming variables..
For each of the factors (variables), any single landscape can be represented in a combination of the following possible values: one or zero for each of the factors:
1 If factor i is existent
Xi =
0 If factor i is not existent
Using the dummy variables as regressors and the preference ratings as dependent variables (predicted values), regression models of landscape preference were constructed (see Hardy 1993).
The advantages of this approach of analysis and regressional analysis are: (1) It recognizes the fact that existence/absence (1 or 0) of certain contents is an overriding indictor of perceptual categorization. (2) It avoids a misleading result by imposing unrealistic measurement assumptions on the more or less quantitative and categorical variables. (3) It facilitates the process of categorization. (4) It can be most easily adapted by electronic media, so that the analysis can easily fit into a GIS format, and be integrated into the planning process.